Search results for "Pattern Recognition"
showing 10 items of 2301 documents
Discrimination and selection of new potential antibacterial compounds using simple topological descriptors.
2003
Abstract The aim of the work was to discriminate between antibacterial and non-antibacterial drugs by topological methods and to select new potential antibacterial agents from among new structures. The method used for antibacterial activity selection was a linear discriminant analysis (LDA). It is possible to obtain a QSAR interpretation of the information contained in the discriminant function. We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new antibacterial agents.
Micelles, Rods, Liposomes, and Other Supramolecular Surfactant Aggregates: Computational Approaches
2017
Surfactants are an interesting class of compounds characterized by the segregation of polar and apolar domains in the same molecule. This peculiarity makes possible a whole series of microscopic and macroscopic effects. Among their features, their ability to segregate particles (fluids or entire domains) and to reduce the surface/interfacial tension is the utmost important. The interest in the chemistry of surfactants never weakened; instead, waves of increasing interest have occurred every time a new field of application of these molecules has been discovered. All these special characteristics depend largely on the ability of surfactants to self-assemble and constitute supramolecular struc…
Isolation of the left atrial surface from cardiac multi-detector CT images based on marker controlled watershed segmentation
2006
The delineation of left atrium (LA) and pulmonary veins (PVs) anatomy from high resolution images holds importance for atrial fibrillation (AF) investigation and treatment. In this study, a semiautomatic segmentation procedure for LA and PVs inner surface from contrast enhanced CT data was developed. The procedure consists of a three dimensional marker controlled watershed segmentation applied to the external morphological gradient, followed by variable threshold surface extraction from the original intensity image. A preliminary anisotropic non-linear filtering was implemented to improve the S/N ratio of CT images. The performance of segmentation was evaluated on cardiac CT scans of 12 AF …
Morphometry of Middle Bronze Age palstaves by discrete cosine transform.
2009
9 pages; International audience; The Discrete Cosine Transform (DCT) is a Fourier-related transform widely used in signal processing and well suited to the analysis of open outlines. This method was applied here to evaluate the discrimination power of the inner lateral rib for two palstave populations dating from the Middle Bronze Age, excavated in northwest France. A corpus of almost 400 palstaves (bronze axes) of the Breton and Norman types was processed, and compared to specimens found at Sermizelles in Burgundy. The procedure is robust and produces a discrimination in good agreement with the traditional typology. Besides the definition of a ‘standard' shape for each population, the morp…
Motion Analysis for Dynamic 3D Scene Reconstruction and Understanding
2017
This thesis studies the problem of dynamic scene 3D reconstruction and understanding using a calibrated 2D-3D camera setup mounted on a mobile platform via the analysis of objects' motions. For static scenes, the sought 3D map reconstruction can be obtained by registering the point cloud sequence. However, with dynamic scenes, we require a prior step of moving object elimination, which yields to the motion detection and segmentation problems. We provide solutions for the two practical scenarios, namely the known and unknown camera motion cases, respectively. When camera motion is unknown, our 3D-SSC and 3D-SMR algorithms segment the moving objects by analysing their 3D feature trajectories.…
Motion analysis using the novelty filter
1991
Abstract An original approach to the motion analysis, based on the novelty filter, is proposed. The novelty filter stresses the novelties occurring in a pattern representing an image of the scene under consideration with respect to patterns representing previous images of the same scene, so that visual information about the motion of the objects is obtained. The novelty filter may be implemented by a neural network architecture, taking advantage of the capabilities of massive parallelism, adaptive learning and noise robustness. The novelty filter may learn the entire trajectory of an object, through an incremental learning of a sequence of images capturing the scene, thus emphasizing if the…
Revealing the unique features of each individual's muscle activation signatures
2021
International audience; There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance …
A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders
2016
Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, which compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms that follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extra…
A label compression method for online multi-label classification
2018
Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally…
Multi-label classification using boolean matrix decomposition
2012
This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.